本文關(guān)鍵詞：電動汽車充換電站的優(yōu)化控制策略研究　出處：《山東大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 充換電站 換電需求預(yù)測 優(yōu)化決策 電池荷電狀態(tài) 新能源發(fā)電 不確定性

【摘要】：為了應(yīng)對能源與環(huán)境危機(jī),電動汽車產(chǎn)業(yè)在我國得到了迅猛發(fā)展。電動汽車電能補(bǔ)給配套建立的充換電站,成為電網(wǎng)中的一種新的負(fù)荷類型,其特點(diǎn)在于具有靈活的電力需求特性。由于電動汽車電池的儲能效用,充換電站兼顧了電源與負(fù)荷的雙重屬性,充分挖掘電動汽車充換電站所具有的電能存儲功能,利用其柔性、靈活的負(fù)荷特性,做出合理的充換電決策,不僅有利于對電力系統(tǒng)負(fù)荷波動的平抑,更可以在緊急情況下,為電力系統(tǒng)提供重要的備用支持。因此,研究充換電站與電網(wǎng)運(yùn)行相協(xié)調(diào)的優(yōu)化控制策略具有十分重要的現(xiàn)實(shí)意義。要實(shí)現(xiàn)充換電站充放電的調(diào)控目標(biāo),充換電站的能量預(yù)測與基于預(yù)測結(jié)果的優(yōu)化決策是必不可少的環(huán)節(jié)。其中,充換電站的負(fù)荷預(yù)測可以通過對電動汽車的行駛規(guī)律以及充換電閾值的研究間接實(shí)現(xiàn),也可以利用充換電站歷史運(yùn)行數(shù)據(jù)通過規(guī)律挖掘手段直接建模實(shí)現(xiàn)。而對于充換電站的優(yōu)化決策,一般可以以充換電站的運(yùn)行經(jīng)濟(jì)性或電網(wǎng)的運(yùn)行安全性為決策目標(biāo),并綜合考慮電池容量約束、充電樁功率約束等約束條件,建立充換電站的優(yōu)化調(diào)控模型,得到充換電站充放電功率的優(yōu)化決策結(jié)果。從目前研究現(xiàn)狀來看,充換電站的負(fù)荷預(yù)測及優(yōu)化控制尚存在以下幾個問題：①目前,充換電站負(fù)荷預(yù)測多采用確定性的預(yù)測方法,所得充換電站功率需求序列難以反映預(yù)測結(jié)果的不確定性,無法為制定魯棒性的充電策略提供必要的決策依據(jù)。②現(xiàn)有充換電站的充換電策略優(yōu)化多以連續(xù)的充電功率為控制變量,然而,受電池荷電狀態(tài)及電池?cái)?shù)量的限制,此種模型給出的最優(yōu)解往往難以與現(xiàn)實(shí)對應(yīng)。③對電池交換模式下的風(fēng)、光與充換電站協(xié)同運(yùn)行策略優(yōu)化的研究較少。在上述背景下,本文首先在充換電站換電需求狀態(tài)空間離散化的基礎(chǔ)上,使用馬爾科夫預(yù)測方法對充換電站換電需求進(jìn)行了離散狀態(tài)概率預(yù)測,其所得的換電需求狀態(tài)的概率分布可為充換電站的運(yùn)行決策提供更為全面的決策依據(jù)；其次,文章提出了基于日前分時(shí)電價(jià)的雙向能量交換下的以電池充放臺數(shù)為控制變量的充換電站兩層規(guī)劃方法,其中,第一階段以充換電站的運(yùn)行經(jīng)濟(jì)性為優(yōu)化目標(biāo),第二階段則以第一階段求取的最小費(fèi)用為約束,以滿電電池臺數(shù)最大為目標(biāo)進(jìn)行優(yōu)化,優(yōu)化過程中考慮了負(fù)荷需求的區(qū)間不確定性；最后,在分析充換電站電池交換意愿的基礎(chǔ)上,文章提出了基于物流網(wǎng)的,以電動汽車充換電站與風(fēng)電場側(cè)儲能系統(tǒng)聯(lián)合收益最大化為目標(biāo)的充換電站電池交換模型。所提出的模型與方法均已完成實(shí)際系統(tǒng)構(gòu)建,測試運(yùn)行效果驗(yàn)證了本文所提出方法的有效性。
[Abstract]:In order to deal with the crisis of energy and environment, the electric vehicle industry has developed rapidly in China. Because of the energy storage utility of electric vehicle battery, the charging power station takes into account the dual properties of power supply and load. Fully excavating the electric energy storage function of the electric vehicle charging and replacing power station, making reasonable charging and switching decision by using its flexible and flexible load characteristics is not only conducive to the stabilization of the load fluctuation in the power system. It is also possible to provide important backup support for power systems in emergency situations. It is of great practical significance to study the optimal control strategy of the charging and changing power station in coordination with the power network operation. The aim of charge and discharge regulation and control of the charging and changing power station should be realized. The energy prediction and the optimal decision based on the prediction results are essential links. The load forecasting of charging power station can be realized indirectly by studying the driving law of electric vehicle and the threshold of charging and switching. It is also possible to use the historical operation data of the charging and changing power station to model the model directly through the rule mining method, and to optimize the decision of the charging and changing power station. Generally, the optimal control model of charging power station can be established by taking the economical operation of charging power station or the operation safety of power grid as the decision goal, and considering the constraints of battery capacity and charging pile power, etc. The optimal decision results of charging and discharging power are obtained. According to the current research situation, the following problems exist in the load forecasting and optimal control of the charging and changing power station. The deterministic forecasting method is often used in the load forecasting of recharge power station, and it is difficult to reflect the uncertainty of the forecast result in the power demand series of the recharge and replacement power station. Can not provide the necessary decision basis for the robust charging strategy. 2. The current charging and switching strategy optimization takes the continuous charge power as the control variable, however. Limited by the charging state and the number of batteries, the optimal solution given by this model is often difficult to correspond to the wind in the switching mode of the battery in the actual situation. There are few researches on the optimization of cooperative operation strategy between light and charging power station. Under the above background, the state space of power exchange demand is discretized firstly in this paper. The discrete state probabilistic prediction of the power exchange demand of the recharge power station is carried out by using Markov prediction method. The probability distribution of the state of the power exchange demand can provide a more comprehensive decision basis for the operation decision of the charging and switching power station. Secondly, this paper puts forward a two-layer planning method for charging and discharging power plants based on the bidirectional energy exchange with the number of battery charging and discharging stations as the control variable, which is based on the time-sharing price before the day. The first stage takes the operation economy of the charging and replacing power station as the optimization goal, the second stage takes the minimum cost obtained in the first stage as the constraint, and the maximum number of full battery stations as the goal. The interval uncertainty of load demand is considered in the optimization process. Finally, on the basis of analyzing the willingness of battery exchange in charged power station, the paper puts forward a logistics network based on it. The battery exchange model of electric vehicle charging power station and wind farm side energy storage system is aimed at maximizing the combined income. The proposed model and method have completed the actual system construction. The test results verify the effectiveness of the proposed method.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U469.72;TM715

【共引文獻(xiàn)】

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